






"""simple docstring"""


import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			[]
						embed.append(
						    (
						        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
						        F"""stage{idx}.patch_embed.proj.weight""",
						    ) )
						embed.append(
						    (
						        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
						        F"""stage{idx}.patch_embed.proj.bias""",
						    ) )
						embed.append(
						    (
						        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
						        F"""stage{idx}.patch_embed.norm.weight""",
						    ) )
						embed.append(
						    (
						        F"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
						        F"""stage{idx}.patch_embed.norm.bias""",
						    ) )
						return embed


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			[]
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
						        F"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
						    ) )
						attention_weights.append(
						    (
						        F"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
						        F"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
						    ) )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", F"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", F"""stage{idx}.blocks.{cnt}.norm1.weight""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", F"""stage{idx}.blocks.{cnt}.norm1.bias""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", F"""stage{idx}.blocks.{cnt}.norm2.weight""") )
						attention_weights.append(
						    (F"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", F"""stage{idx}.blocks.{cnt}.norm2.bias""") )
						return attention_weights


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			[]
						token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token') )
						return token


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :str						=			[]
						head.append(('layernorm.weight', 'norm.weight') )
						head.append(('layernorm.bias', 'norm.bias') )
						head.append(('classifier.weight', 'head.weight') )
						head.append(('classifier.bias', 'head.bias') )
						return head


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[Any]						=			'imagenet-1k-id2label.json'
						lowerCAmelCase   :Dict						=			10_00

						lowerCAmelCase   :Any						=			'huggingface/label-files'
						lowerCAmelCase   :Tuple						=			num_labels
						lowerCAmelCase   :Optional[Any]						=			json.load(open(cached_download(hf_hub_url(a__     ,      a__     ,      repo_type='dataset' ) )     ,      'r' ) )
						lowerCAmelCase   :Union[str, Any]						=			{int(a__ ): v for k, v in idalabel.items()}

						lowerCAmelCase   :Dict						=			idalabel
						lowerCAmelCase   :Dict						=			{v: k for k, v in idalabel.items()}

						lowerCAmelCase   :Dict						=			CvtConfig(num_labels=a__     ,      idalabel=a__     ,      labelaid=a__ )

						# For depth size 13 (13 = 1+2+10)
						if cvt_model.rsplit('/'     ,      1 )[-1][4:6] == "13":
												lowerCAmelCase   :List[str]						=			[1, 2, 10]

						# For depth size 21 (21 = 1+4+16)
						elif cvt_model.rsplit('/'     ,      1 )[-1][4:6] == "21":
												lowerCAmelCase   :Dict						=			[1, 4, 16]

						# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
						else:
												lowerCAmelCase   :List[str]						=			[2, 2, 20]
												lowerCAmelCase   :Dict						=			[3, 12, 16]
												lowerCAmelCase   :int						=			[1_92, 7_68, 10_24]

						lowerCAmelCase   :Tuple						=			CvtForImageClassification(a__ )
						lowerCAmelCase   :List[Any]						=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' )
						lowerCAmelCase   :Any						=			image_size
						lowerCAmelCase   :Optional[int]						=			torch.load(a__     ,      map_location=torch.device('cpu' ) )

						lowerCAmelCase   :List[str]						=			OrderedDict()
						lowerCAmelCase   :Optional[Any]						=			[]

						for idx in range(len(config.depth ) ):
												if config.cls_token[idx]:
																		lowerCAmelCase   :List[Any]						=			list_of_state_dict + cls_token(a__ )
												lowerCAmelCase   :Tuple						=			list_of_state_dict + embeddings(a__ )
												for cnt in range(config.depth[idx] ):
																		lowerCAmelCase   :str						=			list_of_state_dict + attention(a__     ,      a__ )

						lowerCAmelCase   :str						=			list_of_state_dict + final()
						for gg in list_of_state_dict:
												print(a__ )
						for i in range(len(a__ ) ):
												lowerCAmelCase   :Tuple						=			original_weights[list_of_state_dict[i][1]]

						model.load_state_dict(a__ )
						model.save_pretrained(a__ )
						image_processor.save_pretrained(a__ )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		parser.add_argument(
		    '--cvt_model',
		    default='cvt-w24',
		    type=str,
		    help='Name of the cvt model you\'d like to convert.',
		)
		parser.add_argument(
		    '--image_size',
		    default=3_84,
		    type=int,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--cvt_file_name',
		    default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
		    type=str,
		    help='Input Image Size',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







"""simple docstring"""


from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 						__UpperCamelCase     (     UpperCamelCase						):
					@staticmethod
					@abstractmethod
					def        UpperCAmelCase__ (							UpperCAmelCase							:	ArgumentParser   )	->       Optional[Any]:
											raise NotImplementedError()
					@abstractmethod
					def        UpperCAmelCase__ (							self							:	str   )	->       List[str]:
											raise NotImplementedError()